Due to their ability to consider spatial information, CAEs have been especially useful when analyzing imaging data.
A deep CAE was used to cluster the embedded features of imaging data of several benchmark datasets [1]}.
The method, coined Deep Convolutional Embedded Clustering, can create features that were used to cluster the MNIST handwritten digits with an accuracy of \(0.8897\) .
Some other CAE applications include removing artifacts in images, denoising images, and coloring grayscale images.
Here we use a CAE to learn skin features.
